Sean F. Everton

Disrupting Dark Networks (eBook, ePUB)

• Format: ePub

Produktbeschreibung

Disrupting Dark Networks focuses on how social network analysis can be used to craft strategies to track, destabilize and disrupt covert and illegal networks. The book begins with an overview of the key terms and assumptions of social network analysis and various counterinsurgency strategies. The next several chapters introduce readers to algorithms and metrics commonly used by social network analysts. They provide worked examples from four different social network analysis software packages (UCINET, NetDraw, Pajek and ORA) using standard network data sets as well as data from an actual terrorist network that serves as a running example throughout the book. The book concludes by considering the ethics of and various ways that social network analysis can inform counterinsurgency strategizing. By contextualizing these methods in a larger counterinsurgency framework, this book offers scholars and analysts an array of approaches for disrupting dark networks.

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Produktdetails

• Verlag: Cambridge University Press
• Erscheinungstermin: 12. November 2012
• Englisch
• ISBN-13: 9781139794084
• Artikelnr.: 52955095

Autorenporträt

Sean F. Everton is Assistant Professor in the Department of Defense Analysis, as well as the Co-Director of the CORE Lab at the Naval Postgraduate School.

Inhaltsangabe

Part I. Introduction: 1. Social network analysis: an introduction
2. Strategic options for disrupting dark networks
Part II. Social Network Analysis: Techniques: 3. Getting started with UCINET, NetDraw, Pajek, and ORA
4. Gathering, recording, and manipulating social networks
Part III. Social Network Analysis: Metrics: 5. Network topography
6. Cohesion and clustering
7. Centrality, power, and prestige
8. Brokers, bridges, and structural holes
9. Positions, roles, and blockmodels
Part IV. Social Network Analysis: Advances: 10. Dynamic analyses of dark networks
11. Statistical models for dark networks
Part V. Conclusion: 12. The promise and limits of social network analysis.